Patent Similarity Data and Innovation Metrics

2020 ◽  
Vol 17 (3) ◽  
pp. 615-639
Author(s):  
Ryan Whalen ◽  
Alina Lungeanu ◽  
Leslie DeChurch ◽  
Noshir Contractor
Keyword(s):  
Similarity Data ◽  
Innovation Metrics

Patent Similarity Data and Innovation Metrics

2020 ◽  
Author(s):  
Ryan Whalen ◽  
Alina Lungeanu ◽  
Leslie DeChurch ◽  
Noshir Contractor
Keyword(s):  
Similarity Data ◽  
Innovation Metrics

Signposts of Innovation: A Review of Innovation Metrics

2017 ◽  
Author(s):  
Xiaohui (Janet) Hao ◽  
Bart van Ark
Keyword(s):  
Innovation Metrics

scholarly journals The Free-Linking Task: A graph-inspired method for generating non-disjoint similarity data with food products

2021 ◽  
pp. 104355
Author(s):  
Jacob Lahne ◽  
Katherine Phetxumphou ◽  
Marino Tejedor-Romero ◽  
David Orden
Keyword(s):  
Food Products ◽  
Similarity Data

Innovation Metrics and Indicators as Inputs to Strategy

2016 ◽  
pp. 83-96
Author(s):  
Jerry Krill
Keyword(s):  
Innovation Metrics

scholarly journals Minimum description length and psychological clustering models

2019 ◽  
Author(s):  
Michael David Lee ◽  
Danielle Navarro
Keyword(s):  
Data Analysis ◽  
Goodness Of Fit ◽  
Minimum Description Length ◽  
Additive Clustering ◽  
Similarity Data ◽  
Additive Trees ◽  
Using Data

Clustering is one of the most basic and useful methods of data analysis. This chapter describes a number of powerful clustering models, developed in psychology, for representing objects using data that measure the similarities between pairs of objects. These models place few restrictions on how objects are assigned to clusters,and allow for very general measures of the similarities between objects and clusters.Geometric Complexity Criteria (GCC) are derived for these models, and are used to fit the models to similarity data in a way that balances goodness-of-fit with complexity. Complexity analyses, based on the GCC, are presented for the two most widely used psychological clustering models, known as “additive clustering”and “additive trees”

Abstract 682: Drug repurposing engine fueled by diverse drug similarity data

2019 ◽  
Author(s):  
Jamal A. Elkhader ◽  
Coryandar M. Gilvary ◽  
Neel S. Madhukar ◽  
Olivier Elemento ◽  
David Solit
Keyword(s):  
Drug Repurposing ◽  
Similarity Data ◽  
Drug Similarity

Q-SpAM: How to Efficiently Measure Similarity in Online Research

2020 ◽  
pp. 004912412091493 ◽  
Author(s):  
Alex Koch ◽  
Felix Speckmann ◽  
Christian Unkelbach
Keyword(s):  
Social Science ◽  
Spatial Arrangement ◽  
Online Research ◽  
Stimulus Similarity ◽  
Social Scientists ◽  
Large Stimulus ◽  
Computer Screen ◽  
Similarity Data ◽  
User Friendly ◽  
Do So

Measuring the similarity of stimuli is of great interest to a variety of social scientists. Spatial arrangement by dragging and dropping “more similar” targets closer together on the computer screen is a precise and efficient method to measure stimulus similarity. We present Qualtrics-spatial arrangement method (Q-SpAM), a feature-rich and user-friendly online version of spatial arrangement. Combined with crowdsourcing platforms, Q-SpAM provides fast and affordable access to similarity data even for large stimulus sets. Participants may spatially arrange up to 100 words or images, randomly selected targets, self-selected targets, self-generated targets, and targets self-marked in different colors. These and other Q-SpAM features can be combined. We exemplify how to collect, process, and visualize similarity data with Q-SpAM and provide R and Excel scripts to do so. We then illustrate Q-SpAM’s versatility for social science, concluding that Q-SpAM is a reliable and valid method to measure the similarity of lots of stimuli with little effort.

Impressions in the Dark: A Multidimensional Scaling Approach to Multi-Modal Experience

1976 ◽  
Vol 42 (3_suppl) ◽  
pp. 1031-1036
Author(s):  
Noreen Webb
Keyword(s):  
Multidimensional Scaling ◽  
Two Dimensions ◽  
Nonmetric Multidimensional Scaling ◽  
Auditory Stimuli ◽  
Similarity Data ◽  
Musical Chords

12 students rated the experienced similarities among 16 compound visual-auditory stimuli. Each of 4 colors was combined with each of 4 musical chords to form 16 color-chord impressions. Nonmetric multidimensional scaling of the 16 × 16 matrix of similarity data yielded an orderly and interpretable solution in two dimensions. The results suggest that the color-chord combinations produced psychologically integrated impressions which varied along orthogonal dimensions of darkness (versus lightness) and spread (versus compactness) of chords.

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